




'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					int = 10_00    ):

      """simple docstring"""






      return sum(e for e in range(3      ,__UpperCamelCase    ) if e % 3 == 0 or e % 5 == 0    )


if __name__ == "__main__":
      print(F"""{solution() = }""")



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCamelCase__  =					{
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts )
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config )
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights )

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False
  elif args.student_type == "gpt2":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[str]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Training" )
  parser.add_argument("--force"    ,     action="store_true"    ,     help="Overwrite dump_path if it already exists." )

  parser.add_argument(
      "--dump_path"    ,     type=a__    ,     required=a__    ,     help="The output directory (log, checkpoints, parameters, etc.)" )
  parser.add_argument(
      "--data_file"    ,     type=a__    ,     required=a__    ,     help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."    ,     )

  parser.add_argument(
      "--student_type"    ,     type=a__    ,     choices=["distilbert", "roberta", "gpt2"]    ,     required=a__    ,     help="The student type (DistilBERT, RoBERTa)."    ,     )
  parser.add_argument("--student_config"    ,     type=a__    ,     required=a__    ,     help="Path to the student configuration." )
  parser.add_argument(
      "--student_pretrained_weights"    ,     default=a__    ,     type=a__    ,     help="Load student initialization checkpoint." )

  parser.add_argument(
      "--teacher_type"    ,     choices=["bert", "roberta", "gpt2"]    ,     required=a__    ,     help="Teacher type (BERT, RoBERTa)." )
  parser.add_argument("--teacher_name"    ,     type=a__    ,     required=a__    ,     help="The teacher model." )

  parser.add_argument("--temperature"    ,     default=2.0    ,     type=a__    ,     help="Temperature for the softmax temperature." )
  parser.add_argument(
      "--alpha_ce"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the distillation loss. Must be >=0." )
  parser.add_argument(
      "--alpha_mlm"    ,     default=0.0    ,     type=a__    ,     help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."    ,     )
  parser.add_argument("--alpha_clm"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the CLM loss. Must be >=0." )
  parser.add_argument("--alpha_mse"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the MSE loss. Must be >=0." )
  parser.add_argument(
      "--alpha_cos"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the cosine embedding loss. Must be >=0." )

  parser.add_argument(
      "--mlm"    ,     action="store_true"    ,     help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
  parser.add_argument(
      "--mlm_mask_prop"    ,     default=0.15    ,     type=a__    ,     help="Proportion of tokens for which we need to make a prediction."    ,     )
  parser.add_argument("--word_mask"    ,     default=0.8    ,     type=a__    ,     help="Proportion of tokens to mask out." )
  parser.add_argument("--word_keep"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to keep." )
  parser.add_argument("--word_rand"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to randomly replace." )
  parser.add_argument(
      "--mlm_smoothing"    ,     default=0.7    ,     type=a__    ,     help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."    ,     )
  parser.add_argument("--token_counts"    ,     type=a__    ,     help="The token counts in the data_file for MLM." )

  parser.add_argument(
      "--restrict_ce_to_mask"    ,     action="store_true"    ,     help="If true, compute the distillation loss only the [MLM] prediction distribution."    ,     )
  parser.add_argument(
      "--freeze_pos_embs"    ,     action="store_true"    ,     help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."    ,     )
  parser.add_argument(
      "--freeze_token_type_embds"    ,     action="store_true"    ,     help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."    ,     )

  parser.add_argument("--n_epoch"    ,     type=a__    ,     default=3    ,     help="Number of pass on the whole dataset." )
  parser.add_argument("--batch_size"    ,     type=a__    ,     default=5    ,     help="Batch size (for each process)." )
  parser.add_argument(
      "--group_by_size"    ,     action="store_false"    ,     help="If true, group sequences that have similar length into the same batch. Default is true."    ,     )

  parser.add_argument(
      "--gradient_accumulation_steps"    ,     type=a__    ,     default=50    ,     help="Gradient accumulation for larger training batches."    ,     )
  parser.add_argument("--warmup_prop"    ,     default=0.05    ,     type=a__    ,     help="Linear warmup proportion." )
  parser.add_argument("--weight_decay"    ,     default=0.0    ,     type=a__    ,     help="Weight decay if we apply some." )
  parser.add_argument("--learning_rate"    ,     default=5e-4    ,     type=a__    ,     help="The initial learning rate for Adam." )
  parser.add_argument("--adam_epsilon"    ,     default=1e-6    ,     type=a__    ,     help="Epsilon for Adam optimizer." )
  parser.add_argument("--max_grad_norm"    ,     default=5.0    ,     type=a__    ,     help="Max gradient norm." )
  parser.add_argument("--initializer_range"    ,     default=0.02    ,     type=a__    ,     help="Random initialization range." )

  parser.add_argument(
      "--fp16"    ,     action="store_true"    ,     help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"    ,     )
  parser.add_argument(
      "--fp16_opt_level"    ,     type=a__    ,     default="O1"    ,     help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )    ,     )
  parser.add_argument("--n_gpu"    ,     type=a__    ,     default=1    ,     help="Number of GPUs in the node." )
  parser.add_argument("--local_rank"    ,     type=a__    ,     default=-1    ,     help="Distributed training - Local rank" )
  parser.add_argument("--seed"    ,     type=a__    ,     default=56    ,     help="Random seed" )

  parser.add_argument("--log_interval"    ,     type=a__    ,     default=500    ,     help="Tensorboard logging interval." )
  parser.add_argument("--checkpoint_interval"    ,     type=a__    ,     default=4_000    ,     help="Checkpoint interval." )
  _UpperCamelCase           =    parser.parse_args()
  sanity_checks(a__ )

  # ARGS #
  init_gpu_params(a__ )
  set_seed(a__ )
  if args.is_master:
    if os.path.exists(args.dump_path ):
      if not args.force:
        raise ValueError(
            f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
            " itUse `--force` if you want to overwrite it" )
      else:
        shutil.rmtree(args.dump_path )

    if not os.path.exists(args.dump_path ):
      os.makedirs(args.dump_path )
    logger.info(f'Experiment will be dumped and logged in {args.dump_path}' )

    # SAVE PARAMS #
    logger.info(f'Param: {args}' )
    with open(os.path.join(args.dump_path    ,     "parameters.json" )    ,     "w" ) as f:
      json.dump(vars(a__ )    ,     a__    ,     indent=4 )
    git_log(args.dump_path )

  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.student_type]
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  _UpperCamelCase           =    teacher_tokenizer_class.from_pretrained(args.teacher_name )
  _UpperCamelCase           =    {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    _UpperCamelCase           =    tokenizer.all_special_tokens.index(a__ )
    _UpperCamelCase           =    tokenizer.all_special_ids[idx]
  logger.info(f'Special tokens {special_tok_ids}' )
  _UpperCamelCase           =    special_tok_ids
  _UpperCamelCase           =    tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(f'Loading data from {args.data_file}' )
  with open(args.data_file    ,     "rb" ) as fp:
    _UpperCamelCase           =    pickle.load(a__ )

  if args.mlm:
    logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)' )
    with open(args.token_counts    ,     "rb" ) as fp:
      _UpperCamelCase           =    pickle.load(a__ )

    _UpperCamelCase           =    np.maximum(a__    ,     1 ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      _UpperCamelCase           =    0.0  # do not predict special tokens
    _UpperCamelCase           =    torch.from_numpy(a__ )
  else:
    _UpperCamelCase           =    None

  _UpperCamelCase           =    LmSeqsDataset(params=a__    ,     data=a__ )
  logger.info("Data loader created." )

  # STUDENT #
  logger.info(f'Loading student config from {args.student_config}' )
  _UpperCamelCase           =    student_config_class.from_pretrained(args.student_config )
  _UpperCamelCase           =    True

  if args.student_pretrained_weights is not None:
    logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}' )
    _UpperCamelCase           =    student_model_class.from_pretrained(args.student_pretrained_weights    ,     config=a__ )
  else:
    _UpperCamelCase           =    student_model_class(a__ )

  if args.n_gpu > 0:
    student.to(f'cuda:{args.local_rank}' )
  logger.info("Student loaded." )

  # TEACHER #
  _UpperCamelCase           =    teacher_model_class.from_pretrained(args.teacher_name    ,     output_hidden_states=a__ )
  if args.n_gpu > 0:
    teacher.to(f'cuda:{args.local_rank}' )
  logger.info(f'Teacher loaded from {args.teacher_name}.' )

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(a__    ,     a__ )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(a__    ,     a__ )

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0 ) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  _UpperCamelCase           =    Distiller(
      params=a__    ,     dataset=a__    ,     token_probs=a__    ,     student=a__    ,     teacher=a__ )
  distiller.train()
  logger.info("Let's go get some drinks." )


if __name__ == "__main__":
   main()




